Overview

Dataset statistics

Number of variables20
Number of observations53257
Missing cells0
Missing cells (%)0.0%
Total size in memory8.1 MiB
Average record size in memory160.0 B

Variable types

Numeric13
Categorical7

Alerts

rec_online_8 has constant value "0.0" Constant
sum_recharge is highly correlated with recharge_frequency and 4 other fieldsHigh correlation
recharge_frequency is highly correlated with sum_recharge and 4 other fieldsHigh correlation
rec_online_10 is highly correlated with sum_recharge and 2 other fieldsHigh correlation
rec_online_15 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
sos_rec_5 is highly correlated with sum_recharge and 2 other fieldsHigh correlation
rec_online_20_b2 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
sum_recharge is highly correlated with recharge_frequency and 4 other fieldsHigh correlation
recharge_frequency is highly correlated with sum_recharge and 4 other fieldsHigh correlation
rec_online_10 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
rec_online_15 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
sos_rec_5 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
rec_online_20_b2 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
sum_recharge is highly correlated with recharge_frequency and 4 other fieldsHigh correlation
recharge_frequency is highly correlated with sum_recharge and 4 other fieldsHigh correlation
rec_online_10 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
rec_online_15 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
sos_rec_5 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
rec_online_20_b2 is highly correlated with sum_recharge and 1 other fieldsHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
chip_pre_rec_20 is highly correlated with rec_online_8High correlation
rec_online_100_b18 is highly correlated with rec_online_8High correlation
sos_rec_3 is highly correlated with rec_online_8High correlation
venda is highly correlated with rec_online_8High correlation
pct_rec_1190 is highly correlated with rec_online_8High correlation
rec_online_8 is highly correlated with chip_pre_rec_20 and 5 other fieldsHigh correlation
chip_pre_rec_10 is highly correlated with rec_online_8High correlation
sum_recharge is highly correlated with recharge_frequency and 4 other fieldsHigh correlation
recharge_frequency is highly correlated with sum_recharge and 5 other fieldsHigh correlation
rec_online_10 is highly correlated with recharge_frequency and 1 other fieldsHigh correlation
rec_online_15 is highly correlated with sum_recharge and 3 other fieldsHigh correlation
sos_rec_5 is highly correlated with sum_recharge and 5 other fieldsHigh correlation
rec_online_20_b2 is highly correlated with sum_recharge and 3 other fieldsHigh correlation
rec_online_13 is highly correlated with recharge_frequency and 1 other fieldsHigh correlation
rec_online_50_b8 is highly correlated with sum_rechargeHigh correlation
pct_rec_1190 is highly correlated with pct_rec_690High correlation
pct_rec_690 is highly correlated with pct_rec_1190High correlation
rec_online_50_b8 is highly skewed (γ1 = 27.03772441) Skewed
pct_rec_690 is highly skewed (γ1 = 30.75651164) Skewed
pct_rec_sos_5 is highly skewed (γ1 = 106.1640427) Skewed
sum_recharge has 36396 (68.3%) zeros Zeros
recharge_frequency has 36196 (68.0%) zeros Zeros
rec_online_10 has 44846 (84.2%) zeros Zeros
rec_online_35_b5 has 52537 (98.6%) zeros Zeros
rec_online_15 has 45727 (85.9%) zeros Zeros
sos_rec_5 has 45816 (86.0%) zeros Zeros
rec_online_20_b2 has 46324 (87.0%) zeros Zeros
rec_online_13 has 50701 (95.2%) zeros Zeros
rec_online_50_b8 has 52928 (99.4%) zeros Zeros
rec_online_30_b4 has 51710 (97.1%) zeros Zeros
rec_online_40_b6 has 52780 (99.1%) zeros Zeros
pct_rec_690 has 53128 (99.8%) zeros Zeros
pct_rec_sos_5 has 53240 (> 99.9%) zeros Zeros

Reproduction

Analysis started2022-03-15 15:21:09.861123
Analysis finished2022-03-15 15:21:28.247916
Duration18.39 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

sum_recharge
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct431
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.64598269
Minimum0
Maximum1133
Zeros36396
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:28.311699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile145
Maximum1133
Range1133
Interquartile range (IQR)20

Descriptive statistics

Standard deviation57.2915604
Coefficient of variation (CV)2.23393898
Kurtosis25.77404542
Mean25.64598269
Median Absolute Deviation (MAD)0
Skewness3.832534439
Sum1365828.1
Variance3282.322893
MonotonicityNot monotonic
2022-03-15T12:21:28.401400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036396
68.3%
201201
 
2.3%
101014
 
1.9%
15995
 
1.9%
30798
 
1.5%
40729
 
1.4%
60632
 
1.2%
35521
 
1.0%
45505
 
0.9%
50485
 
0.9%
Other values (421)9981
 
18.7%
ValueCountFrequency (%)
036396
68.3%
31
 
< 0.1%
5153
 
0.3%
6.929
 
0.1%
101014
 
1.9%
11.930
 
0.1%
13217
 
0.4%
13.86
 
< 0.1%
15995
 
1.9%
1855
 
0.1%
ValueCountFrequency (%)
11331
< 0.1%
11301
< 0.1%
10411
< 0.1%
10251
< 0.1%
8091
< 0.1%
8031
< 0.1%
7561
< 0.1%
7401
< 0.1%
7001
< 0.1%
6791
< 0.1%

recharge_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.977937173
Minimum0
Maximum111
Zeros36196
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:28.483131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11
Maximum111
Range111
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.529962748
Coefficient of variation (CV)2.290246025
Kurtosis36.25302536
Mean1.977937173
Median Absolute Deviation (MAD)0
Skewness4.352737132
Sum105339
Variance20.52056249
MonotonicityNot monotonic
2022-03-15T12:21:28.560870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036196
68.0%
12960
 
5.6%
22412
 
4.5%
32121
 
4.0%
41586
 
3.0%
51183
 
2.2%
61060
 
2.0%
7805
 
1.5%
8702
 
1.3%
9609
 
1.1%
Other values (55)3623
 
6.8%
ValueCountFrequency (%)
036196
68.0%
12960
 
5.6%
22412
 
4.5%
32121
 
4.0%
41586
 
3.0%
51183
 
2.2%
61060
 
2.0%
7805
 
1.5%
8702
 
1.3%
9609
 
1.1%
ValueCountFrequency (%)
1111
 
< 0.1%
961
 
< 0.1%
891
 
< 0.1%
851
 
< 0.1%
691
 
< 0.1%
662
< 0.1%
641
 
< 0.1%
623
< 0.1%
611
 
< 0.1%
603
< 0.1%

rec_online_10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5946823892
Minimum0
Maximum33
Zeros44846
Zeros (%)84.2%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:28.636617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.03403893
Coefficient of variation (CV)3.420378621
Kurtosis36.63970165
Mean0.5946823892
Median Absolute Deviation (MAD)0
Skewness5.289392529
Sum31671
Variance4.137314369
MonotonicityNot monotonic
2022-03-15T12:21:28.703393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
044846
84.2%
12955
 
5.5%
21538
 
2.9%
3963
 
1.8%
4674
 
1.3%
5453
 
0.9%
6347
 
0.7%
7261
 
0.5%
8257
 
0.5%
9197
 
0.4%
Other values (23)766
 
1.4%
ValueCountFrequency (%)
044846
84.2%
12955
 
5.5%
21538
 
2.9%
3963
 
1.8%
4674
 
1.3%
5453
 
0.9%
6347
 
0.7%
7261
 
0.5%
8257
 
0.5%
9197
 
0.4%
ValueCountFrequency (%)
332
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
294
< 0.1%
282
< 0.1%
273
< 0.1%
262
< 0.1%
253
< 0.1%
242
< 0.1%
232
< 0.1%

rec_online_35_b5
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02407195298
Minimum0
Maximum9
Zeros52537
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:28.765187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2447871637
Coefficient of variation (CV)10.16897814
Kurtosis268.907612
Mean0.02407195298
Median Absolute Deviation (MAD)0
Skewness14.24979011
Sum1282
Variance0.05992075553
MonotonicityNot monotonic
2022-03-15T12:21:28.816017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
052537
98.6%
1399
 
0.7%
2169
 
0.3%
3107
 
0.2%
420
 
< 0.1%
515
 
< 0.1%
65
 
< 0.1%
73
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
052537
98.6%
1399
 
0.7%
2169
 
0.3%
3107
 
0.2%
420
 
< 0.1%
515
 
< 0.1%
65
 
< 0.1%
73
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
73
 
< 0.1%
65
 
< 0.1%
515
 
< 0.1%
420
 
< 0.1%
3107
 
0.2%
2169
 
0.3%
1399
 
0.7%
052537
98.6%

rec_online_15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4019565503
Minimum0
Maximum36
Zeros45727
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:28.880798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.489663929
Coefficient of variation (CV)3.706032225
Kurtosis71.88689742
Mean0.4019565503
Median Absolute Deviation (MAD)0
Skewness6.881118575
Sum21407
Variance2.21909862
MonotonicityNot monotonic
2022-03-15T12:21:28.947572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
045727
85.9%
13257
 
6.1%
21570
 
2.9%
3935
 
1.8%
4525
 
1.0%
5334
 
0.6%
6214
 
0.4%
7174
 
0.3%
8116
 
0.2%
990
 
0.2%
Other values (22)315
 
0.6%
ValueCountFrequency (%)
045727
85.9%
13257
 
6.1%
21570
 
2.9%
3935
 
1.8%
4525
 
1.0%
5334
 
0.6%
6214
 
0.4%
7174
 
0.3%
8116
 
0.2%
990
 
0.2%
ValueCountFrequency (%)
361
 
< 0.1%
351
 
< 0.1%
331
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
273
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
231
 
< 0.1%
222
< 0.1%

sos_rec_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4022382034
Minimum0
Maximum48
Zeros45816
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:29.019333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.475077451
Coefficient of variation (CV)3.667173923
Kurtosis104.882123
Mean0.4022382034
Median Absolute Deviation (MAD)0
Skewness7.549238736
Sum21422
Variance2.175853485
MonotonicityNot monotonic
2022-03-15T12:21:29.091093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
045816
86.0%
13048
 
5.7%
21509
 
2.8%
3990
 
1.9%
4559
 
1.0%
5433
 
0.8%
6260
 
0.5%
7184
 
0.3%
8137
 
0.3%
990
 
0.2%
Other values (24)231
 
0.4%
ValueCountFrequency (%)
045816
86.0%
13048
 
5.7%
21509
 
2.8%
3990
 
1.9%
4559
 
1.0%
5433
 
0.8%
6260
 
0.5%
7184
 
0.3%
8137
 
0.3%
990
 
0.2%
ValueCountFrequency (%)
481
 
< 0.1%
441
 
< 0.1%
421
 
< 0.1%
341
 
< 0.1%
292
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
243
< 0.1%

rec_online_20_b2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3316371557
Minimum0
Maximum29
Zeros46324
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:29.154879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.181725052
Coefficient of variation (CV)3.563307161
Kurtosis52.20915257
Mean0.3316371557
Median Absolute Deviation (MAD)0
Skewness5.912989462
Sum17662
Variance1.396474098
MonotonicityNot monotonic
2022-03-15T12:21:29.215678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
046324
87.0%
13046
 
5.7%
21513
 
2.8%
3857
 
1.6%
4536
 
1.0%
5312
 
0.6%
6250
 
0.5%
7154
 
0.3%
881
 
0.2%
952
 
0.1%
Other values (13)132
 
0.2%
ValueCountFrequency (%)
046324
87.0%
13046
 
5.7%
21513
 
2.8%
3857
 
1.6%
4536
 
1.0%
5312
 
0.6%
6250
 
0.5%
7154
 
0.3%
881
 
0.2%
952
 
0.1%
ValueCountFrequency (%)
291
 
< 0.1%
261
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%
192
 
< 0.1%
173
 
< 0.1%
164
 
< 0.1%
158
< 0.1%
147
< 0.1%
1317
< 0.1%

chip_pre_rec_10
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
52526 
 
731

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
52526
98.6%
731
 
1.4%

Length

2022-03-15T12:21:29.280461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:21:29.319331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
52526
98.6%
731
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

chip_pre_rec_20
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
53005 
 
243
 
8
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Common Values

ValueCountFrequency (%)
53005
99.5%
243
 
0.5%
8
 
< 0.1%
1
 
< 0.1%

Length

2022-03-15T12:21:29.360252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:21:29.402053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
53005
99.5%
243
 
0.5%
8
 
< 0.1%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rec_online_13
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09534896821
Minimum0
Maximum42
Zeros50701
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:29.450891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6324765806
Coefficient of variation (CV)6.633281853
Kurtosis657.1545204
Mean0.09534896821
Median Absolute Deviation (MAD)0
Skewness17.96037187
Sum5078
Variance0.400026625
MonotonicityNot monotonic
2022-03-15T12:21:29.509694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
050701
95.2%
11568
 
2.9%
2434
 
0.8%
3232
 
0.4%
4138
 
0.3%
560
 
0.1%
643
 
0.1%
727
 
0.1%
817
 
< 0.1%
910
 
< 0.1%
Other values (13)27
 
0.1%
ValueCountFrequency (%)
050701
95.2%
11568
 
2.9%
2434
 
0.8%
3232
 
0.4%
4138
 
0.3%
560
 
0.1%
643
 
0.1%
727
 
0.1%
817
 
< 0.1%
910
 
< 0.1%
ValueCountFrequency (%)
421
 
< 0.1%
281
 
< 0.1%
251
 
< 0.1%
221
 
< 0.1%
212
< 0.1%
172
< 0.1%
161
 
< 0.1%
152
< 0.1%
141
 
< 0.1%
133
< 0.1%

rec_online_50_b8
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01083425653
Minimum0
Maximum11
Zeros52928
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:29.569494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1759514914
Coefficient of variation (CV)16.24029216
Kurtosis1024.826145
Mean0.01083425653
Median Absolute Deviation (MAD)0
Skewness27.03772441
Sum577
Variance0.03095892733
MonotonicityNot monotonic
2022-03-15T12:21:29.624359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
052928
99.4%
1201
 
0.4%
278
 
0.1%
325
 
< 0.1%
59
 
< 0.1%
65
 
< 0.1%
45
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
052928
99.4%
1201
 
0.4%
278
 
0.1%
325
 
< 0.1%
45
 
< 0.1%
59
 
< 0.1%
65
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
101
 
< 0.1%
81
 
< 0.1%
73
 
< 0.1%
65
 
< 0.1%
59
 
< 0.1%
45
 
< 0.1%
325
 
< 0.1%
278
 
0.1%
1201
0.4%

rec_online_30_b4
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04868843532
Minimum0
Maximum12
Zeros51710
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:29.679143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3589232183
Coefficient of variation (CV)7.371837192
Kurtosis207.9429036
Mean0.04868843532
Median Absolute Deviation (MAD)0
Skewness12.13972265
Sum2593
Variance0.1288258767
MonotonicityNot monotonic
2022-03-15T12:21:29.740924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
051710
97.1%
11021
 
1.9%
2284
 
0.5%
3124
 
0.2%
450
 
0.1%
526
 
< 0.1%
618
 
< 0.1%
712
 
< 0.1%
85
 
< 0.1%
93
 
< 0.1%
Other values (3)4
 
< 0.1%
ValueCountFrequency (%)
051710
97.1%
11021
 
1.9%
2284
 
0.5%
3124
 
0.2%
450
 
0.1%
526
 
< 0.1%
618
 
< 0.1%
712
 
< 0.1%
85
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
93
 
< 0.1%
85
 
< 0.1%
712
 
< 0.1%
618
 
< 0.1%
526
 
< 0.1%
450
0.1%
3124
0.2%

rec_online_40_b6
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01470229266
Minimum0
Maximum10
Zeros52780
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:29.797734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1864559261
Coefficient of variation (CV)12.68209867
Kurtosis481.2980427
Mean0.01470229266
Median Absolute Deviation (MAD)0
Skewness18.64721571
Sum783
Variance0.03476581239
MonotonicityNot monotonic
2022-03-15T12:21:29.852548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
052780
99.1%
1308
 
0.6%
285
 
0.2%
354
 
0.1%
418
 
< 0.1%
66
 
< 0.1%
55
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
052780
99.1%
1308
 
0.6%
285
 
0.2%
354
 
0.1%
418
 
< 0.1%
55
 
< 0.1%
66
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
66
 
< 0.1%
55
 
< 0.1%
418
 
< 0.1%
354
 
0.1%
285
 
0.2%
1308
 
0.6%
052780
99.1%

pct_rec_1190
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
53142 
 
107
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
53142
99.8%
107
 
0.2%
8
 
< 0.1%

Length

2022-03-15T12:21:29.911349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:21:29.951216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
53142
99.8%
107
 
0.2%
8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pct_rec_690
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004055804871
Minimum0
Maximum6
Zeros53128
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:29.990086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09445374625
Coefficient of variation (CV)23.28853317
Kurtosis1205.386794
Mean0.004055804871
Median Absolute Deviation (MAD)0
Skewness30.75651164
Sum216
Variance0.00892151018
MonotonicityNot monotonic
2022-03-15T12:21:30.039919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
053128
99.8%
171
 
0.1%
237
 
0.1%
317
 
< 0.1%
62
 
< 0.1%
42
 
< 0.1%
ValueCountFrequency (%)
053128
99.8%
171
 
0.1%
237
 
0.1%
317
 
< 0.1%
42
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
42
 
< 0.1%
317
 
< 0.1%
237
 
0.1%
171
 
0.1%
053128
99.8%

rec_online_100_b18
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
53234 
 
18
 
3
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
53234
> 99.9%
18
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%

Length

2022-03-15T12:21:30.100716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:21:30.140582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
53234
> 99.9%
18
 
< 0.1%
3
 
< 0.1%
2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pct_rec_sos_5
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0006196368552
Minimum0
Maximum7
Zeros53240
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:21:30.181446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0443984783
Coefficient of variation (CV)71.65241694
Kurtosis13895.33239
Mean0.0006196368552
Median Absolute Deviation (MAD)0
Skewness106.1640427
Sum33
Variance0.001971224876
MonotonicityNot monotonic
2022-03-15T12:21:30.236263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
053240
> 99.9%
110
 
< 0.1%
23
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
053240
> 99.9%
110
 
< 0.1%
23
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
41
 
< 0.1%
32
 
< 0.1%
23
 
< 0.1%
110
 
< 0.1%
053240
> 99.9%

sos_rec_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
53256 
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Common Values

ValueCountFrequency (%)
53256
> 99.9%
1
 
< 0.1%

Length

2022-03-15T12:21:30.297059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:21:30.335931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
53256
> 99.9%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rec_online_8
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
53257 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
53257
100.0%

Length

2022-03-15T12:21:30.376793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:21:30.415663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
53257
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

venda
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
40490 
12767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
40490
76.0%
12767
 
24.0%

Length

2022-03-15T12:21:30.454533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:21:30.493404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
40490
76.0%
12767
 
24.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-15T12:21:26.499759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.025877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.192972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.201599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.307899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.272672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.291265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.394576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.408786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.385519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.514801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.519385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.500103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.577499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.119562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.269716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.277345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.381653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.349416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.365019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.472316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.482539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.460269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.592483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.594133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.576847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.655239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.203280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.348448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.355086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.455406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.427156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.440766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.553046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.559283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.534022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.669226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.668883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.658573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.733976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.289989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.427189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.434819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.534143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.506889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.517514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.629789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.634033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.611765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.745969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.745627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.734320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.807729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.373712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.503932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.510566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.603909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.581635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.589269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.703542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.704799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.683523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.820719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.818383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.809070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.889456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.461416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.582669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.591353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.682646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.663366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.669004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.786266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.784529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.762260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.904439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.898116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.887806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.971185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.544139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.659412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.670033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.754406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.741106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.742756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.864002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.858283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.836013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.979189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.970873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.963552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:27.055899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.627862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.738149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.749766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.830153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.821837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.939099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.941746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.932036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.913753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.056929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.048613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.040296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:27.136630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.785336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.811902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.822522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.899919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.895589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.011851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.017488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.002799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.984516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.132679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.122367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.114051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:27.217360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.871049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.886648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.898269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.972672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.972332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.087602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.096230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.077549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.059266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.208422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.196119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.189796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:27.309053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:14.951779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.966381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.977006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.047422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.053059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.165343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-15T12:21:23.138003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-15T12:21:25.271866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-15T12:21:22.231037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.211756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.361909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.347613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.343282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:27.669850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:15.109253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:16.119873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:17.229158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:18.195925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:19.210536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:20.317832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:21.329055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:22.308776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:23.435009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:24.439649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:25.422364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:26.421023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-15T12:21:30.714663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-15T12:21:30.875126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-15T12:21:31.022632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-15T12:21:31.121303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.